Internalized Plans for Communication-Sensitive Robot Team Behaviors
Alan R.Wagner, Ronald C. Arkin
Mobile Robot Laboratory, College of Computing
Georgia Institute of Technology, Atlanta, USA, {alan.wagner, arkin}@cc.gatech.edu
Abstract
Autonomous teams of robots operating in a dynamic,
adversarial environment stand to benefit from using all
available resources. But how can knowledge be used to
construct a plan that does not interfere with the robots
ability to react to its environment? In this research we
distill abstract representation into a plan usable by a
reactive behavior-based architecture. This plan is then
exploited to enhance the performance of a team of
robots tasked with maintaining communications while
performing reconnaissance. Utilizing multiple plans in
serial and in parallel is shown via simulation to be a
promising method for increasing mission performance.
We conclude that the utility of these internalized plans
warrants further investigation as a method for imbuing
reactive agents with a priori knowledge.
1. Introduction
Reconnaissance, like surveillance, is a dynamic,
distributed sensory problem in an adversarial
environment demanding a strongly cooperative
solution to achieve a goal [13]. These problems require
agents capable of coordinated sensing, processing, and
communication [7].
Purely reactive robotic architectures, such as those
advocated by Brooks [3], allow a robot to operate in
dynamic environments but abandon traditional
planning and knowledge representation. Yet a variety
of information—such as terrain and logistics advice—
is available that could aid robotic reconnaissance
missions. Clearly, if information from these sources
could be accessed in a manner that did not hinder the
robotic team's reactive performance it would assist the
mission.
Several hybrid deliberative/reactive architectures have
been developed in an attempt to address the shortcomings of purely reactive robotic architectures
[1,5,12]. Other approaches attempt to morph higherlevel plans into forms suitable for reactive behaviors
[8, 9, 10].
Payton also delineates a method for using information
in a reactive architecture [14]. In this method, a priori
knowledge becomes an enabling resource for decisionmaking. From his perspective, traditional plans are
artificially abstracted from knowledge that often results
in over- or under-specification of a mission’s
objectives. By minimizing symbolic abstraction, one
can develop a plan for action that may be used directly
by a reactive agent. Payton brands this type of plan an
internalized plan. These internalized plans differ from
traditional plans in their lack of symbols and their tight
representational coupling to the needs of a reactive
robot. Moreover, the plans are used only as advice,
injecting world map or other types of knowledge only
at the discretion of the robot.
In this research, we employ Payton's notion of
internalized plans to fortify reactive control with a
priori map knowledge. We then use this knowledge to
coordinate the communication and coverage objectives
of a team of robots performing a reconnaissance
mission in a simulated urban setting. This hybrid
deliberative/reactive approach is integrated with
Arkin's behavior-based motor schema architecture [2]
using the MissionLab [11] behavior specification
software.
2. The Architecture
The goal of this research is to produce a team of
heterogeneous mobile robots capable of coordinated
self-sustaining communication while simultaneously
satisfying mission requirements. It is hypothesized
that by utilizing plans as resources a robotic team's
performance will improve on specific communication
and coverage metrics in a reconnaissance task. As
mentioned above this system was implemented on a
hybrid deliberative/reactive architecture employing
Arkin's motor schemas. In the following sections we
detail the methods used for the creation and utilization
of map knowledge as internalized plans for coordinated
team behavior.
2.1 Generating Internalized Plans
Prior to a mission, a uniform cost search algorithm [15]
is employed to generate cost values for each nonobstacle cell of a grid mesh. Because the distance from
any cell to a neighboring cell is assigned a cost of
Figure 1: The mission specification for the leftmost
robot in the serial plans experiment (Section 2.4).
either 1 or √2, the values associated with a location
represent an approximation of the distance to a goal
location rather than the exact straight-line distance
from one point to another. The grid mesh for which the
cost values have been generated constitutes an
internalized plan.
Software was developed that generates internalized
plans based on a list of obstacles, if any exist, and the
goal. Currently the system operates on both
MissionLab style overlay formats [6] and MITRE
specified OPAR formats [4]. A 250 by 250 meter grid,
at 1 by 1 meter resolution, internalized plan can be
completed in approximately 20 seconds using standard
1Ghz PC hardware. Upon completion the software
generates a resource file that can be stored in a
database or shipped along with the robot executable for
immediate use on the robot itself. This resource file
may contain any number of plans based on different
sets of constraints (obstacles, communications, etc).
Finally, using the MissionLab behavior specification
system (Fig. 1), a robot configuration is generated that
incorporates the internalized plan behaviors. These
behaviors prescribe which plan to use and the manner
in which to use it.
2.2 Using Internalized Plans
During mission execution, the robot uses its current
location in space to index an internalized plan that
produces a movement vector towards the minimal
adjacent grid cell. This imposes a requirement of robot
localization to use the plan effectively. Fortunately for
the outdoor environments for which this DARPA
project is intended, GPS can provide this information
reasonably accurately. The resulting vector advises the
robot to move in only one of eight possible directions.
Although this produces sub-optimal advice it greatly
Figure 2: A vector field from an internalized plan
reduces the practical and computational complexity of
determining the grid cost values. Remember also that
the plan is only advice that is injected into the actual
robot controller that contains the collection of other
active behaviors, such as move-to-goal, avoidobstacles, noise, etc. This tends to smooth the
digitization bias present in the internalized plan. Figure
2 depicts a typical internalized plan.
2.3 Plans in Parallel
As discussed by Payton [14], internalized plans offer
an advantage in that several plans can be utilized
simultaneously. We incorporate multiple plans in two
ways: in parallel and in serial. Figure 3 depicts the use
of parallel internalized plans. Serial plans are discussed
in Section 2.4.
Parallel plans consist of more than one internalized
plan, each with the same goal location but differing in
the constraints on which the plan is based. Moreover
selecting a plan (or fusing them) among several in
parallel occurs in a single time step. A parallel plan is
created by first generating the least constrained plan
for that goal-environment pair. Next, additional
constraints are added in the form of obstacles to the
internalized plan and the plan generation algorithm is
rerun. This process can be continued for as many
constraints as desired. Determining which vector to
output can be achieved in a number of ways. One
choice is to select vectors from the most constrained
internalized plan that affords a viable route to the goal.
This strategy is extremely simple and easily
implemented, but rather inflexible. Alternatively cost
functions with tunable gains can be created that
represent each constraint. This method can greatly
increase the systems flexibility and could allow for
advice vectors that represent a mission’s dynamically
evolving constraints. Because plans with additional
Internalized Plan 2
Query advice
Output
advice
Vector
Query advice
Internalized Plan 1
Vector
selection
mechanism
t0
Figure 3: Plans used in Parallel: Advice is queried
from plan 2, which results in an output vector. The
output occurs in time t0.
constraints contain all of the known obstacles from
plans with fewer constraints, the robot is never led into
an obstacle occurring in a less constrained plan.
Moreover, determining if a plan has a route to the goal
is accomplished instantly as any plan that does will
return a finite cost value. This selection mechanism can
either be used statically or dynamically. Static
selection chooses the most constrained plan in advance
and then follows that plan to completion. Dynamic
selection reevaluates which internalized plan should be
selected and utilized at each time step.
We investigated the use of statically selected parallel
plans. In this case, a parallel plan consisted of one
internalized plan depicting the placement of physical
obstacles in the environment and one internalized plan
further constrained by communication attenuating
areas. For the results reported in this paper, the
experimenter
visually
selected
potential
communications attenuating areas such as alleys,
parked cars, etc. Ideally, in future work a selection
mechanism will be based on actual automated terrain
analysis.
2.4 Plans in Serial
Another way to exploit multiple internalized plans is to
utilize each plan in serial. In this case each plan may
maintain a different goal location and a perceptual
trigger determines which plan to use during different
time steps. Figure 4 graphically depicts a serial plan.
Perhaps the most intuitive example of plans in serial is
a contingency plan. Regarding this communicationsrelated research, a simple contingency plan might
consist of a location for the team to meet at if
communications fail or degrade, or before moving on
to another section of the environment. Perceptual
triggers such as being located at a specific point or
recognition of a communications failure, for example,
transition the robot to query advice from a different
internalized plan.
Output advice
Vector
Internalized Plan 1
Query advice
Perceptual
Trigger
t0
Output advice
Vector
Internalized Plan 2
t1
Figure 4: Plans used in Serial: Advice is first queried
from plan 1. Some perceptual trigger causes the
transition from plan 1 to plan 2. Thereafter advice is
queried from plan 2. The output from each plan occurs
in during a unique time step.
The use of seven different plans in serial was
investigated. Each robot utilized a unique subset of
these seven plans to improve coverage while also
maintaining communication. Initially every robot
queried a plan that guided them to unique locations that
would maximize the coverage of a large (250m by
50m) rectangular area. Upon reaching the location a
perceptual trigger invokes another plan that directs all
robots back to a common point located at the entrance
to a street. Once communication is re-established the
robots utilize different plans to maximize coverage as
they traverse a narrow pathway to the goal.
3. Performance Metrics
We defined several metrics that characterize
performance in real world reconnaissance operations.
As maintaining robust communication connectivity is a
primary focus of this study, funded under DARPA's
MARS Vision 2020 program, the amount of time the
team operated as a single communicating network, the
amount of time each robot is connected to another
robot, and the overall connectivity was logged in our
current simulation studies. Ideally there would only be
one connected network within the team. Whether full
connectivity is desired will depend on other mission
constraints, such as the desirability of coverage,
relative to the importance of maintaining
communication.
Coverage is another important criterion for evaluating
reconnaissance missions. In general, it is assumed that
the probability of discovering a target increases with
increased coverage of the terrain. For this study, it is
further assumed that each robot had the ability to sense
a three by three meter area in all directions from a
point location, excluding obstacles.
Figure 5: Reactive behaviors guide the robots through
the environment. Each robot first goes to a waypoint
and then proceeds to the goal (not shown).
Figure 7: Plans are used in parallel. In this case the
most constraining plan avoids communicationattenuating locations by circumventing the buildings.
Figure 6: Navigation using an internalized plan
constructed with only obstacle knowledge guides the
robots. As in the previous experiment, each robot
proceeds to a waypoint before going to the goal.
Figure 8: Serial plans first cover the initial open area
before regrouping and following the street located in
the center to the goal.
Time to complete the mission and the overall distance
traveled are also considered for metrics, as successful
reconnaissance may correlate to timeliness in mission
completion and the total distance traveled is an indirect
measure of energy expended.
4. Experiments
To evaluate the system four experiments were
performed on a simulated section of the Georgia Tech
Campus. Three simulated robots were tasked with
navigating from a start location to a goal location
approximately 250 meters away. A start position was
randomly chosen along the x (bottom) axis. Figures 5-8
depict the environment and the routes taken during a
typical run of each experiment. For the experiments
shown in Figures 5 and 6 the robots were required to
stop at intermediate waypoints (shown in the figures)
to assure initial dispersion. A detailed network
attenuation model has also been integrated into the
MissionLab system for accurate simulation of
networking failures. This model currently reduces the
communication signal strength when a robot is
occluded by terrain. Thirty trials per experiment were
run. All experiments included a small amount of
random noise behavior (output vector gain of .11).
To establish baseline performance initially the use of
purely reactive behaviors was first examined (Fig. 5);
where each robot's behavioral specification consisted
of only reactive GoTo behaviors. This behavior
produces a single instantaneous vector resulting from
the summation of the attraction to the goal (or the
designated waypoints), repulsion from obstacles and
the small contribution from the noise behavior. This
behavior is purely reactive. As stated earlier, each
robot in this experiment proceeded to a single
intermediate point before moving onto the goal.
In the next set of experiments, the use of
communications-insensitive internal plans was
examined. Three experiments were conducted
involving different proportions of using plans as
advice. In the first experiment a 0.66 gain adjusted
reactive vector was summed with a 0.34 gain adjusted
planning vector. A second experiment summed a 0.34
gain adjusted reactive vector with a 0.66 gain adjusted
planning vector. The final experiment consisted of only
using the planning vector. These plan vectors simply
reflected the location of physical barriers on a map.
Although the start, intermediate, and endpoints for this
experiment are identical to those of the earlier
experiments, the actual route taken by the robot is in
part determined by the robot's plan rather then the
instantaneous potential field. The performance on all
metrics was expected to be about the same whether
using purely reactive behaviors or communications
insensitive plans.
Next the use of plans in parallel (Fig. 7) was examined.
Here two plans were developed: One described paths
to the goal circumventing only the physical obstacles,
the second described paths to the goal circumventing
both the physical obstacles and also the areas likely to
attenuate communication. Upon beginning the mission
the robot determines if the most constrained plan can
be implemented, where a constraint might refer to
communication attenuation, fuel reserves, or terrain
coverage. If so, it uses this plan. If not, it uses the next
less constrained plan. We tested only the case in which
the more constrained of the two plans could be utilized.
Finally, the use of plans in serial (Fig. 8) was
examined. A succession of plans was selected so as to
afford improved terrain coverage. The robots initially
employ a plan that guides them in different directions
to ensure coverage. Upon reaching a predefined area, a
perceptual trigger transitions the robot to use a
different plan. This plan directs each robot toward a
predefined checkpoint. From here they move through a
narrow (~25 meter) street. There is no centralized
robot- to-robot coordination enforced, rather each robot
operates independently. Again the robots use their
internalized plans with spread goal locations to
maximize coverage while also exploiting specially
defined perceptual triggers to maintain communication
links to the other robots (Fig. 1). These triggers query
MissionLab’s network attenuation model to determine
if a robot can communicate with another robot. If so
the robot proceeds toward its goal. If not, the robot
stops and waits until communication is re-established.
As currently implemented this behavior is somewhat
deadlock prone, yet it suffices as a proof of concept for
multiple internal plans used in serial. Additional
behavior specifications, such as one that causes the
robot to wander when communication is lost or a
simple timeout mechanism, would quickly solve any
deadlock problems.
5. Results
It was conjectured that by utilizing plans as resources a
robotic team's performance would improve on
communication and coverage metrics in a simulated
reconnaissance task. The results demonstrate that
internalized plans can be used to improve the team's
ability to communicate and that additional resources
(e.g. reliable knowledge) improve performance when
integrated smoothly into a behavioral controller.
Figures 9 and 10 compare the results obtained using
various proportions of plan advice. Figures 9 and 11
compare the percent time each robot team maintained a
single complete mobile ad hoc network. Figures 10 and
12 compare the mean area covered per experiment.
Finally, Figures 11 and 12 compare the use of
additional map resources in the form of serial and
parallel internalized plans when compared to reactive
behaviors alone and plans without additional map
resources.
As indicated by figure 9, no significant difference in
the ability to communicate (0.51 < p < .98 for all
comparisons) exists when using communications
insensitive plans. Some significant differences in
coverage, however, do result (fig. 10). Specifically,
using reactive behaviors alone results in significantly
greater coverage (p < .04 in all cases). This increase in
coverage is likely the result of differences in obstacle
navigation. Plans guide the robots through obstacle
fields in a near optimal manner. Reactive behaviors, on
the other hand, approach an obstacle until repulsive
forces guide them around the object. Thus, the robot
may loiter around the obstacle gaining small bits of
coverage before finding a path past object. No
significant difference in coverage was observed when
varying the proportion of planning included (.08 < p <
.97).
Although not directly comparable, a significant (vs.
reactive p = 0 for both) and drastic difference occurs
when communication-sensitive internalized plans are
incorporated (fig. 11 and 12). The communications
planning in serial experiment strives to improve
coverage
while
also
maintaining
adequate
communications via checkpoints and perceptual
triggers. Although a significant reduction in
communication performance occurs (p = 0) when plans
are used in serial vs. parallel, this difference represents
the mission specification and not the use of internal
plans per se.
Figure 12 presents results for the mean area covered by
communication-sensitive planning, communicationinsensitive planning, and using reactive behaviors
Percent Time as One Network
Percent Time as One Network
120
100
30
Percent Mission Time
Percent Mission Time
35
25
20
15
10
5
80
60
40
20
0
0
Reactive
66:34
React vs.
Plan
Reactive
No Comm.
34:66
React vs. Planning
Plan
Comm.
Planning
Serial
Figure 11: Mean Total Area covered per experiment.
The use of additional resources in the form of
internalized plans results in less coverage although
serial plans aid coverage when compared to a single
parallel plan.
Mean Total Area Covered
4500
4000
3500
3000
2500
2000
1500
1000
500
0
4500
Reactive
66:34
34:66 No Comm.
React vs. React vs. Planning
Plan
Plan
Experiment
Figure 10: Percent time as a single network per
experiment. The use of additional resources in the form
of internalized plans results in a drastic improvement
in communications.
alone. The reactive and communications-insensitive
planning experiments result in the greatest coverages.
In these cases, the robots essentially diverge pursuing
their own directions relative to the goal unconstrained
by communications concerns. In the communications
planning in parallel experiment, a route for each robot
is generated that avoids communication-attenuating
areas while navigating to the goal. This route is the
same for all robots, thus resulting in near perfect
communications
and
poor
coverage.
The
communications planning in serial experiment
somewhat alleviates this by guiding the robots to
explore specific areas of the environment. Therefore,
although we cannot claim that serial plans improve
Area Covered (1m x 1m)
Area Covered (1m x 1m)
Mean Total Area Covered
Comm.
Planning
Parallel
Experiment
Experiment
Figure 9: Percent time as a single network per
experiment. The lack of additional resources in the
form of internalized plans results in no significant
improvement.
No Comm.
Planning
4000
3500
3000
2500
2000
1500
1000
500
0
Reactive
No Comm.
Planning
Comm.
Planning
Parallel
Comm.
Planning
Serial
Experiment
Figure 12: Mean Total Area covered per experiment.
The use of additional resources in the form of
internalized plans result in less coverage although
serial plans aid coverage when compared to a single
parallel plan.
coverage, we do maintain that a suitably constructed
behavioral specification that utilizes internalized plans
can result in improved coverage and improved
communications. Moreover, in all cases the coverage
statistics assume that the robot can only sense (or
provide coverage for) in its immediate 3 by 3 meter
vicinity. This is probably an underestimate. If we
assume larger areas of detection visibility the
differences in coverage will likely become less
Table 1: Results on performance metrics relevant to reconnaissance*
Time (time
steps)
66.6
11.6
49.5
4.3
45.3
4.0
53.8
8.7
60.6
13.0
116.8
23.7
Reactive Mean:
Standard Deviation:
66:34 React. Vs. Plan
Standard Deviation:
34:66 React. Vs. Plan
Standard Deviation:
Plan No Comms:
Standard Deviation:
Plan Comms Parallel:
Standard Deviation:
Plan Comms Serial:
Standard Deviation:
t-Test Reactive Vs. 66:34 React. Vs. Plan
"
"
Vs. 34:66 React. Vs. Plan
"
"
Vs. Plan No Comms
t-Test 66:34 Vs. 34:66 React. Vs. Plan
"
" "
Plan No Comms
t-Test 34:66 Vs. Plan No Comms
t-Test Plan Comms. Parallel Vs. Plan Comms. Serial
Distance (meters)
834.7
80.2
797.0
71.4
800.0
71.2
820.8
71.5
1017.8
313.1
915.6
69.3
Time connected (time
steps)
1435.7
180.1
1408.8
173.3
1372.6
146.2
1347.5
128.0
2051.9
343.7
3560.9
317.5
Percent time fully
connected (time steps)
21.0
2.8
20.6
2.4
20.6
2.4
20.4
2.7
98.9
0.8
76.3
7.0
P=0
P=0
P=0
P=0
P = 0.02
P=0
P = 0.06
P = 0.08
P = 0.4786
P = 0.87
P = 0.20
P = 0.26
P = 0.56
P = 0.14
P = 0.0339
P = 0.39
P = 0.12
P = 0.48
P = 0.56
P = 0.56
P = 0.4013
P = 1.0
P = 0.76
P = 0.76
P=0
P = 0.0897
P=0
P=0
*N = 30 for all trials. Confidence Interval = 95%. P values are double sided. P = 0 when t >3.014 or P < 0.
significant. Future experimentation should lend proof
to this claim.
Table 1 depicts the robot team's performance on the
other reconnaissance metrics. Regarding the use of
communications-sensitive plans versus reactive
behaviors, the time required to complete a mission
increases when the plan guides each robot to explore
more terrain. Distance traveled increases when using
communication-sensitive planning. This is a reflection
of the plans themselves. The time each robot is
connected via communications to other robots
increases with communications-sensitive planning.
Finally, the percentage time as a fully connected
network reflects the general trend in connectivity
already observed: communications-sensitive planning
helps maintain communications.
Table 1 also provides the results from hypothesis
testing of reactive behaviors only vs. reactive planning
mixes. For most metrics, different proportions of
communications-insensitive planning do not make a
significant difference. The sole exception is mission
time. In this case, adding some amount of planning
decreased the time to complete a mission significantly.
On the other hand, solely plan-driven missions required
significantly more time than reactive plan mixes.
6. Conclusions and Future Work
We conclude that additional resources in the form of
communications-sensitive internalized plans aid
performance on the studied communications metrics.
These additional resources help the robot select a path
that will necessarily improve these metrics. It should
be noted that there is no attempt to supplant reactive
control with a priori planned control. Rather the robot
is merely offered additional resources for use in
determining its trajectory. As the paths produced by the
reactive-behaviors-only and the communicationsinsensitive planning experiments were based on similar
resources, it is not surprising that they had similar
results. Yet, the fact that these two dissimilar methods
generated nearly identical results is in itself revealing.
This seems to indicate that when confronted with the
same environment, the internalized plan method selects
a path exceedingly similar to that of purely reactive
methods.
On the other hand, when some degree of planning was
included, the time required to complete a mission
decreased significantly versus using either reactive
behaviors or planning alone. This decrease in mission
time is likely due to the combination of the plan
shuttling the robot through obstacles and the reactive
behaviors smoothing digitization bias.
The experiments employing parallel and serial plans
illustrate two potential methods for maximizing
performance on the specified communication and
coverage metrics. Strictly speaking these experiments
cannot be directly compared to the other
experiments—the paths chosen were based on different
resources. Even so, as methods for generating
additional resources for the robot, the parallel and
serial techniques discussed above are shown to be
effective.
Thus far we have assumed that the environment meets
two criteria: 1) the robot’s internal knowledge of the
environment accurately reflects the actual environment,
and 2) the robot is capable of localizing itself within
the environment. Since internalized plans are used as
advice, we suspect that these two criteria are malleable
or may not be necessary at all. In future work we
intend to explore this possibility.
Other future work will attempt to reproduce these
findings using situated physical robots in an actual
urban environment. We also hope to investigate
merging reactive/deliberate techniques for coping with
unexpected obstacles. Finally, we would like to
investigate algorithmic methods for determining areas
of terrain maps that might be communication
attenuating and include the cost functions that best
describe these areas.
7. Summary
The extent to which reactive plans improve the
communication and coverage of a simulated robotic
team in an urban environment has been investigated.
Improvements in communication occur when planning
includes communication-attenuating factors. Parallel
internalized plans may be more appropriate when
communications constraints cannot be relaxed.
Moreover, parallel plans offer the possibility of adding
additional constraint knowledge to a single internalized
plan. Serial internalized plans, on the other hand,
afford a method for employing different plans in
different situations. Although coverage degrades when
using communications-focused plans, the use of plans
in serial offers hope for improving coverage while also
maintaining communications. Therefore we conclude
that by including all available knowledge resources, a
robotic team’s performance can be made to improve on
specific communication and coverage metrics in a
reconnaissance task.
Acknowledgments
This research is funded under DARPA/DOI contract
#NBCH1020012 as part of the MARS Vision 2020
program that is a joint effort of Georgia Tech, UPenn,
USC and BBN.
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